2014/05/15 by Yichao Lu, Lu, Yichao, Dean P. Foster +1
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #stat.ML
paper · pdf · doi:10.48550/arxiv.1405.3952
arxiv created 2014/05/15 · openalex publication_date 2014/05/15 · arxiv updated 2014/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in terms of both prediction accuracy and computational efficiency compared with other large scale regression algorithms like Gradient Descent, Stochastic Gradient Descent and Principal Component Regression on both simulated and real datasets.